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computer-vision-9

Transparent objects

3260 papers • 126 benchmarks • 313 datasets

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Benchmarks

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Libraries

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Datasets

TRansPose

Transparent Object Images | Indoor Object Dataset

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Most implemented papers

TOM-Net: Learning Transparent Object Matting from a Single Image

Guanying Chen, Kwan-Yee Kenneth Wong, Kai Han•Mon Mar 12 2018

A deep learning framework, called TOM-Net, is proposed for learning the refractive flow of transparent object matting, which comprises two parts, namely a multi-scale encoder-decoder network for producing a coarse prediction, and a residual network for refinement.

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KeyPose: Multi-View 3D Labeling and Keypoint Estimation for Transparent Objects

K. Konolige, A. Angelova, Xingyu Liu, Rico Jonschkowski•Wed Dec 04 2019

This paper establishes an easy method for capturing and labeling 3D keypoints on desktop objects with an RGB camera, and develops a deep neural network, called KeyPose, that learns to accurately predict object poses using 3DKeypoints, from stereo input, and works even for transparent objects.

132 0

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Natural Image Matting via Guided Contextual Attention

Yaoyi Li, Hongtao Lu•Sun Jan 12 2020

This work develops a novel end-to-end approach for natural image matting with a guided contextual attention module, which is specifically designed for image matts and can mimic information flow of affinity-based methods and utilize rich features learned by deep neural networks simultaneously.

188 0

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Segmenting Transparent Objects in the Wild

Chunhua Shen, Wenhai Wang, Enze Xie, P. Luo, Wenjia Wang, Mingyu Ding•Mon Mar 30 2020

A novel boundary-aware segmentation method, termed TransLab, is proposed, which exploits boundary as the clue to improve segmentation of transparent objects and significantly outperforms 20 recent object segmentation methods based on deep learning.

206 0

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FakeMix Augmentation Improves Transparent Object Detection

Enze Xie, Qibin Hou, Yang Cao, Zhengqiang Zhang, Kai Zhao, Xiangui Luo, Jian Tuo•Tue Mar 23 2021

This work elaborately generates boundaries of transparent objects from other samples into the current image during training, which adjusts the data space and improves the generalization of the models, and presents AdaptiveASPP, an enhanced version of ASPP that can capture multi-scale and cross-modality features dynamically.

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RGB-D Local Implicit Function for Depth Completion of Transparent Objects

D. Fox, Yu Xiang, A. Mousavian, Luyang Zhu, H. Mazhar, Jozef van Eenbergen, Shoubhik Debnath•Wed Mar 31 2021

A new approach for depth completion of transparent objects from a single RGB-D image using a local implicit neural representation built on ray-voxel pairs that allows the method to generalize to unseen objects and achieve fast inference speed.

116 0

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Through the Looking Glass: Neural 3D Reconstruction of Transparent Shapes

Manmohan Chandraker, Zhengqin Li, Yu-Ying Yeh•Tue Apr 21 2020

This work proposes a physically-based network to recover 3D shape of transparent objects using a few images acquired with a mobile phone camera, under a known but arbitrary environment map and renders a synthetic dataset to encourage the model to learn refractive light transport across different views.

78 0

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Transparent objects | State-of-the-Art